The increasing use of wireless personal networks in educational institutions has created significant challenges in ensuring network security and the reliable testing of communication infrastructure. The selection of appropriate software tools for network security testing is a complex decision-making problem due to multiple software quality criteria and operational requirements. This paper proposes a multi-criteria model for evaluating approaches to wireless personal network security testing in educational institutions through the analysis of representative software tools. The evaluation framework is based on the ISO/IEC 25010 software quality criteria: reliability, functional suitability, interoperability, performance efficiency and scalability, compatibility and maintainability. Five widely used tools (Nmap, OpenVAS, Nessus, Wireshark and Wazuh) were analyzed using a structured multi-criteria approach. Criteria weights were determined using the PIPRECIA-S method, while the ranking was verified using the TOPSIS method. The results show that Wazuh achieved the highest overall score (0.3051), followed by Wireshark (0.2315) and Nessus (0.1954), while OpenVAS (0.1443) and Nmap (0.1225) achieved lower ranks. The stability and reliability of the model were confirmed by sensitivity analysis, Pareto analysis, Spearman’s rank correlation and scenario analysis. The model provides a reliable decision-support framework for selecting network security testing approaches in educational and similar organizational environments.

Knowledge of population structure and genetic relationship among inbred lines is essential for exploiting heterosis in maize breeding programs. This study evaluated the concordance between 25k Illumina^® Infinum Maize SNP (Single Nucleotide Polymorphism) Array (25k SNP array)-derived and ribonucleic acid (RNA)-sequencing (seq)-derived markers in estimating genetic relatedness and population structure within the Maize Research Institute “Zemun Polje” (MRIZP) breeding program. A panel of 28 elite MRIZP maize inbred lines, along with two public lines, was analyzed. For the RNA-seq data, three alternative SNP datasets were generated based on heterozygous-site handling (ALL, HOM, and FINAL) to assess their impact on downstream genetic inference. These approaches had distinct effects on clustering resolution and genetic relationship structure. The FINAL dataset, in which heterozygous positions were recoded as missing values and re-filtered, was selected as the most balanced dataset for comparative analyses with 25k SNP array data. Only a limited number of overlapping SNP positions were identified between RNA-seq and 25k SNP array datasets (six in ALL, two in FINAL, and none in HOM), all located within coding regions. Despite this minimal overlap, distance-based analyses revealed partial concordance in genetic relationship patterns and population structure between platforms. Genetic distances estimated from 25k SNP array markers were consistent with pedigree records and provided more informative insights than pedigree data alone. Population structure inferred from 25k SNP array data showed high concordance with previously defined heterotic groups, correctly assigning 29 out of 30 lines (96.67%) to their expected clusters. In contrast, RNA-seq-derived SNPs showed moderate concordance with expected heterotic groupings (56.67%), indicating that transcriptome-derived markers capture part of the underlying breeding structure, but do not fully resolve heterotic group separation. Overall, these results support the 25k SNP array as a robust tool for assessing genetic relatedness and population structure in maize breeding programs, while RNA-seq-derived SNPs provide complementary but less reliable information for routine heterotic assignment.

Smart home systems generate large volumes of data from various sensors, such as temperature, humidity, motion, and energy consumption. Effective preprocessing is essential to enhance data quality, reduce noise, and enable accurate analysis. This paper examines Python-based data preprocessing techniques for smart home environments. The preprocessing workflow includes data cleaning, handling missing values, normalization, feature selection, and data transformation. Data cleaning addresses duplicate records, outliers, and inconsistencies, while missing values are imputed using statistical and machine learning approaches. Normalization techniques standardize sensor readings to ensure consistency across data points. Feature engineering and dimensionality reduction refine the dataset for improved predictive modeling. By enhancing data quality, preprocessing contributes to smarter home automation, efficient anomaly detection, and optimized energy management. This study underscores the critical role of preprocessing in smart home analytics, facilitating reliable and meaningful insights for decision-making. The proposed techniques enhance the integration of smart home data into machine learning models, driving advancements in intelligent home automation systems.

This paper introduces a new hybrid forecasting architecture that combines Nonlinear Autoregressive (NAR) models, the proxy Standardized Precipitation-Evapotranspiration Index (SPEI), and a Nonlinear Autoregressive with Exogenous Inputs (NARX) framework for Soil Moisture Index (SMI) prediction. The suggested methodology solves the crucial difficulty of combining future climatic knowledge into soil moisture forecasting by using a cascaded approach. Stage 1 uses univariate NAR models to create multi-step-ahead predictions of precipitation and temperature. Stage 2 converts these forecasts into proxy SPEI values using a physically based water balance computation, and Stage 3 employs a NARX model that uses observed historical SMI and forecast-derived proxy SPEI as exogenous inputs. The framework is assessed using high-frequency observations from 2014 to 2020, with training data through 2019 and validation covering the whole 2020 horizon. The study combining forecast-driven climatic indicators with autoregressive soil moisture dynamics resulted in prediction accuracy (R² = 0.9888, RMSE = 0.0827, MAE = 0.0567). This study presents a new NAR–SPEI–NARX model for SMI prediction forecasting, based on three-stage modeling, where NAR models forecast precipitation and temperature and then turn them into SPEI-proxy as an exogenous input for NARX.